The increased level of biological detail in individual-based models comes at the cost of more parameters and models rules. The computational complexity of microscopic models—whether they are agent-based, cellular automaton, cellular Potts [1]}, [2]}, vertex models, or something else—also leads to more choices and hidden parameters. (See Buttenschön et al. [3]} for a recent review of computational models of cell behavior.) This raises questions about how choices of computational implementation affect model predictions [4]}, [5]}. However, the stochastic, complex nature of individual-based models makes it challenging to quantitatively describe—let alone robustly compare and relate—model output. On top of this, qualitative observation of a few example simulations may not capture the breadth of patterns that a stochastic model can generate under the same set of parameters. When models of pattern formation become more and more detailed, they hit more and more of the same challenges faced by the underlying biological data that they represent.
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